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[V1][Sampler] Don't apply temp for greedy-only (#13311)
Signed-off-by: Nick Hill <nhill@redhat.com>
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@ -41,8 +41,6 @@ class Sampler(nn.Module):
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logits = self.apply_logits_bias(logits, sampling_metadata)
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# Apply penalties (e.g., min_tokens, freq_penalties).
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logits = self.apply_penalties(logits, sampling_metadata)
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# Apply temperature.
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logits = self.apply_temperature(logits, sampling_metadata.temperature)
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# Sample the next token.
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sampled = self.sample(logits, sampling_metadata)
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@ -82,9 +80,21 @@ class Sampler(nn.Module):
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) -> torch.Tensor:
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assert not (sampling_metadata.all_greedy
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and sampling_metadata.all_random)
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if sampling_metadata.all_greedy:
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return self.greedy_sample(logits)
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if sampling_metadata.all_random:
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greedy_sampled = None
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else:
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greedy_sampled = self.greedy_sample(logits)
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if sampling_metadata.all_greedy:
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return greedy_sampled
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# Apply temperature.
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logits = self.apply_temperature(logits, sampling_metadata.temperature)
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# Apply min_p.
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if not sampling_metadata.no_min_p:
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logits = self.apply_min_p(logits, sampling_metadata.min_p)
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# Apply top_k and/or top_p.
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random_sampled = self.topk_topp_sampler(
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logits,
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sampling_metadata.generators,
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@ -94,13 +104,9 @@ class Sampler(nn.Module):
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sampling_metadata.top_p,
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)
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if not sampling_metadata.no_min_p:
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logits = self.apply_min_p(logits, sampling_metadata.min_p)
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if sampling_metadata.all_random:
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if greedy_sampled is None:
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return random_sampled
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greedy_sampled = self.greedy_sample(logits)
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sampled = torch.where(
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sampling_metadata.temperature < _SAMPLING_EPS,
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greedy_sampled,
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